Didier Concordet

The preanalytic phase in veterinary clinical pathology.

This article presents the general causes of preanalytic variability with a few examples showing specialists and practitioners that special and improved care should be given to this too often neglected phase. The preanalytic phase of clinical pathology includes all the steps from specimen collection to analysis. It is the phase where most laboratory errors occur in human, and probably also in veterinary clinical pathology. Numerous causes may affect the validity of the results, including technical factors, such as the choice of anticoagulant, the blood vessel sampled, and the duration and conditions of specimen handling. While the latter factors can be defined, influence of biologic and physiologic factors such as feeding and fasting, stress, and biologic and endocrine rhythms can often not be controlled. Nevertheless, as many factors as possible should at least be documented. The importance of the preanalytic phase is often not given the necessary attention, although the validity of the results and consequent clinical decision making and medical management of animal patients would likely be improved if the quality of specimens submitted to the laboratory was optimized.

Confidence intervals of reference limits in small reference sample groups.

BACKGROUND Reference intervals are the most common tool used to interpret results of laboratory tests. However, in veterinary clinical pathology, the number of available reference individuals is often small. OBJECTIVES The purpose of this study was to investigate the effects of small reference sample groups on the imprecision of the reference limits. METHODS Gaussian and log-Gaussian distributions of 10 ≤ n ≤ 750 values were analyzed. Reference limits and 90% confidence interval of limits (90% CI) were calculated. Imprecision of limits was estimated by the ratio of the width of the 90% CI: width of the reference interval (WCI/WRI). RESULTS For Gaussian distributions, the WCI/WRI ratio cannot be expected to be lower than 0.2 when n < 55. In log-Gaussian distributions, the ratio greatly increases for the upper limit with skewness toward high values, whereas it moderately decreases for the lower limit. CONCLUSION Independent of the size of the reference sample group, it is very important to report the CIs of the reference limits, which can be very large for small reference sample groups. When the sample size is very small (n < 20), calculations maybe misleading and it is better to instead report all values.

A New Exact Test for the Evaluation of Population Pharmacokinetic and/or Pharmacodynamic Models Using Random Projections

ABSTRACTPurposeWithin-subject dependency of observations has a strong impact on the evaluation of population pharmacokinetic (PK) and/or pharmacodynamic (PD) models. To our knowledge, none of the current model evaluation tools correctly address this issue. We present a new method with a global test and easy diagnostic plot which relies on the use of a random projection technique that allows the analysis of dependent data.MethodsFor each subject, the vector of standardised residuals is calculated and projected onto many random directions drawn uniformly from the unit sphere. Our test compares the empirical distribution of projections with their distribution under the model. Simulation studies assess the level of the test and compare its performance with common metrics including normalised prediction distribution errors and different types of weighted residuals. An application to real data is performed.ResultsIn contrast to other evaluated methods, our test shows adequate level for all models and designs investigated, which confirms its good theoretical properties. The weakness of other methods is demonstrated and discussed.ConclusionsThis new test appears promising and could be used in combination with other tools to drive model evaluation in population PK/PD analyses.

ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra

IntroductionExperiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.ObjectivesIn this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.MethodsA statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.ResultsThe performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.ConclusionASICS is a completely automated procedure to identify and quantify metabolites in 1H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

Establishing bioequivalence of veterinary premixes (Type A medicated articles)

a) Key issues concerning Premix (Type A medicated articles) Bioequivalence evaluations: 1) This is a complex issue concerning both route of administration and formulation. 2) If the animal is not at the bunk/trough, the animal is not self-administering (eating medicated feed), thus there can be no drug absorption. b) Differing opinions among scientists and regulatory authorities/expert bodies regarding: 1) No harmonization on how to design, conduct, and interpret in vivo studies. 2) Applicability of biowaivers to Type A (premix) products. 3) Why are topdress and complete feed considered differently? Are they different formulations or different routes of administration? 4) Single dose vs. multi-dose studies. 5) What is the final formulation? c) What are the next steps: 1) Harmonize current bioequivalence guidelines through the VICH process. 2) Determine the applicability/non-applicability of the Biopharmaceutical Classification System (BCS). 3) Establish the Total Mixed Ration (i.e. formulation) effects. 4) Define the test subject (individual, pen, etc.).

Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed-effects to characterize and quantify variability in drug pharmacokinetics

A common feature of human and veterinary pharmacokinetics is the importance of identifying and quantifying the key determinants of between-patient variability in drug disposition and effects. Some of these attributes are already well known to the field of human pharmacology such as bodyweight, age, or sex, while others are more specific to veterinary medicine, such as species, breed, and social behavior. Identification of these attributes has the potential to allow a better and more tailored use of therapeutic drugs both in companion and food-producing animals. Nonlinear mixed effects (NLME) have been purposely designed to characterize the sources of variability in drug disposition and response. The NLME approach can be used to explore the impact of population-associated variables on the relationship between drug administration, systemic exposure, and the levels of drug residues in tissues. The latter, while different from the method used by the US Food and Drug Administration for setting official withdrawal times (WT) can also be beneficial for estimating WT of approved animal drug products when used in an extralabel manner. Finally, NLME can also prove useful to optimize dosing schedules, or to analyze sparse data collected in situations where intensive blood collection is technically challenging, as in small animal species presenting limited blood volume such as poultry and fish.

Population Pharmacokinetics of Tracers: A New Tool for Medical Imaging?

Positron emission tomography-computed tomography is a medical imaging method measuring the activity of a radiotracer chosen to accumulate in cancer cells. A recent trend of medical imaging analysis is to account for the radiotracer’s pharmacokinetic properties at a voxel (three-dimensional-pixel) level to separate the different tissues. These analyses are closely linked to population pharmacokinetic–pharmacodynamic modelling. Kineticists possess the cultural background to improve medical imaging analysis. This article stresses the common points with population pharmacokinetics and highlights the methodological locks that need to be lifted.

Quantitative assessment of systolic and diastolic right ventricular function by echocardiography and speckle-tracking imaging: a prospective study in 104 dogs

Our aim was (1) to determine the within-day and between-day variability of several indices of systolic and diastolic right ventricular (RV) function by using conventional echocardiography and speckle-tracking echocardiography (STE) (Study 1), (2) to quantify these variables in a large healthy canine population (n = 104) with Doppler-derived estimated systolic pulmonary arterial pressure (SPAP) and left ventricular (LV) function, and (3) to establish the corresponding reference intervals (Study 2). For both studies, RV variables included tricuspid annular plane systolic excursion (TAPSE), right fractional area change (RFAC), STE longitudinal systolic strain (StS) of the RV free wall (RVFW) and of the entire RV (i.e., global RV StS), STE longitudinal systolic RVFW strain rate (SRS), and the diastolic early:late strain rate ratio. All but one within- and between-day coefficients of variation (13/14) were < 15%, the lowest being observed for TAPSE (3.6–9.8%), global RV StS (3.8–9.9%), and RVFW StS (3.7–7.3%). SPAP was weakly and negatively correlated with the TAPSE:body weight ratio (rs = −0.26, p = 0.01) and RVFW SRS (rs = −0.23, p < 0.05). Reference intervals (lower and upper limits with 90% confidence intervals) were provided for all variables. STE provides a non-invasive evaluation of RV function that may be used for clinical investigations in canine cardiology.